MultiQC_miso_batch1_verysensitive

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        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        About MultiQC

        This report was generated using MultiQC, version 1.29

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

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        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        MultiQC_miso_batch1_verysensitive

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/mag analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-12-10, 04:57 UTC based on data in: /mnt/batch/tasks/workitems/job-18b9b3f01a4c32111eda-NFCORE_MAG_MAG_MULTIQC/job-1/nf-0c716a1219e98c7fb2fd4681d0abc4fe/wd

        General Statistics

        Showing 141/141 rows and 16/29 columns.
        Sample Name% Dups (raw)% GC (raw)Avg. length (raw)Median length (raw)M Seqs (raw)% Fails (raw)% Dups (processed)% GC (processed)Avg. length (processed)Median length (processed)M Seqs (processed)% Fails (processed)% Duplication% > Q30Mb Q30 basesReads After FilteringGC content% PF% Adapter% Aligned (Host)% Aligned (Assem.)N50 (Kbp)N50 (Kbp)Assembly Length (Mbp)Assembly Length (Mbp)ContigsBasesCDSOrganism
        MEGAHIT-CONCOCT-group-0_0
        1.7Kbp
        0.0Mbp
        1
        1676
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_1
        4.8Kbp
        3.1Mbp
        757
        3134381
        3120
        Genus species
        MEGAHIT-CONCOCT-group-0_2
        1.8Kbp
        2.0Mbp
        1168
        2031062
        2260
        Genus species
        MEGAHIT-CONCOCT-group-0_3
        14.8Kbp
        0.0Mbp
        2
        16511
        17
        Genus species
        MEGAHIT-CONCOCT-group-0_4
        1.3Kbp
        0.2Mbp
        115
        158173
        114
        Genus species
        MEGAHIT-CONCOCT-group-0_5
        1.5Kbp
        6.6Mbp
        4274
        6634995
        6743
        Genus species
        MEGAHIT-CONCOCT-group-0_6
        2.0Kbp
        0.0Mbp
        28
        49085
        18
        Genus species
        MEGAHIT-CONCOCT-group-0_7
        1.4Kbp
        0.0Mbp
        2
        2404
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_8
        1.0Kbp
        0.0Mbp
        1
        1034
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_9
        3.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_10
        9.2Kbp
        9.4Mbp
        MEGAHIT-CONCOCT-group-0_11
        170.5Kbp
        0.2Mbp
        2
        205308
        255
        Genus species
        MEGAHIT-CONCOCT-group-0_12
        1.8Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_13
        1.1Kbp
        0.0Mbp
        1
        1090
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_14
        1.3Kbp
        0.0Mbp
        2
        2448
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_15
        1.4Kbp
        0.3Mbp
        237
        342090
        404
        Genus species
        MEGAHIT-CONCOCT-group-0_16
        1.7Kbp
        4.1Mbp
        2425
        4058678
        4097
        Genus species
        MEGAHIT-CONCOCT-group-0_17
        4.5Kbp
        0.1Mbp
        18
        57492
        72
        Genus species
        MEGAHIT-CONCOCT-group-0_18
        1.9Kbp
        1.8Mbp
        1010
        1827179
        1883
        Genus species
        MEGAHIT-CONCOCT-group-0_19
        1.5Kbp
        0.0Mbp
        2
        2862
        3
        Genus species
        MEGAHIT-CONCOCT-group-0_20
        1.1Kbp
        0.0Mbp
        2
        2124
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_21
        2.7Kbp
        1.1Mbp
        MEGAHIT-CONCOCT-group-0_22
        239.3Kbp
        0.2Mbp
        1
        239287
        302
        Genus species
        MEGAHIT-CONCOCT-group-0_24
        26.7Kbp
        0.0Mbp
        4
        44342
        66
        Genus species
        MEGAHIT-CONCOCT-group-0_25
        2.0Kbp
        0.0Mbp
        11
        21918
        17
        Genus species
        MEGAHIT-CONCOCT-group-0_26
        1.3Kbp
        0.0Mbp
        4
        4840
        7
        Genus species
        MEGAHIT-CONCOCT-group-0_27
        1.2Kbp
        0.0Mbp
        3
        4415
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_28
        4.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_29
        1.8Kbp
        3.2Mbp
        1799
        3151920
        3029
        Genus species
        MEGAHIT-CONCOCT-group-0_30
        9.2Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_31
        1.0Kbp
        0.0Mbp
        1
        1034
        Genus species
        MEGAHIT-CONCOCT-group-0_32
        25.5Kbp
        2.9Mbp
        174
        2852917
        2903
        Genus species
        MEGAHIT-CONCOCT-group-0_33
        1.7Kbp
        0.0Mbp
        12
        23962
        14
        Genus species
        MEGAHIT-CONCOCT-group-0_34
        2.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_35
        1.3Kbp
        0.0Mbp
        7
        9599
        12
        Genus species
        MEGAHIT-CONCOCT-group-0_36
        2.9Kbp
        0.0Mbp
        3
        5037
        8
        Genus species
        MEGAHIT-CONCOCT-group-0_37
        1.4Kbp
        0.2Mbp
        127
        180142
        132
        Genus species
        MEGAHIT-CONCOCT-group-0_38
        1.6Kbp
        0.0Mbp
        1
        1563
        1
        Genus species
        MEGAHIT-CONCOCT-group-0_39
        7.7Kbp
        4.7Mbp
        666
        4652472
        4835
        Genus species
        MEGAHIT-CONCOCT-group-0_40
        1.4Kbp
        0.0Mbp
        4
        5384
        4
        Genus species
        MEGAHIT-CONCOCT-group-0_41
        1.3Kbp
        0.0Mbp
        17
        24331
        24
        Genus species
        MEGAHIT-CONCOCT-group-0_42
        1.1Kbp
        0.0Mbp
        1
        1117
        Genus species
        MEGAHIT-CONCOCT-group-0_43
        30.4Kbp
        0.0Mbp
        1
        30391
        21
        Genus species
        MEGAHIT-CONCOCT-group-0_44
        14.8Kbp
        7.3Mbp
        813
        7255548
        6886
        Genus species
        MEGAHIT-CONCOCT-group-0_45
        1.2Kbp
        0.0Mbp
        5
        6644
        5
        Genus species
        MEGAHIT-CONCOCT-group-0_46
        14.6Kbp
        2.5Mbp
        MEGAHIT-CONCOCT-group-0_47
        1.4Kbp
        0.0Mbp
        3
        4541
        4
        Genus species
        MEGAHIT-CONCOCT-group-0_48
        5.3Kbp
        3.9Mbp
        804
        3880011
        3704
        Genus species
        MEGAHIT-CONCOCT-group-0_49
        2.2Kbp
        0.0Mbp
        1
        2173
        Genus species
        MEGAHIT-CONCOCT-group-0_50
        1.3Kbp
        0.0Mbp
        1
        1267
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_51
        1.8Kbp
        0.0Mbp
        9
        16659
        19
        Genus species
        MEGAHIT-CONCOCT-group-0_52
        1.3Kbp
        0.0Mbp
        2
        2464
        2
        Genus species
        MEGAHIT-CONCOCT-group-0_53
        1.9Kbp
        0.0Mbp
        14
        26427
        44
        Genus species
        MEGAHIT-CONCOCT-group-0_54
        2.6Kbp
        1.9Mbp
        814
        1941730
        2183
        Genus species
        MEGAHIT-CONCOCT-group-0_55
        136.4Kbp
        4.9Mbp
        74
        4874494
        4598
        Genus species
        MEGAHIT-CONCOCT-group-0_56
        2.7Kbp
        0.0Mbp
        17
        36993
        47
        Genus species
        MEGAHIT-CONCOCT-group-0_57
        1.4Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_58
        1.2Kbp
        0.0Mbp
        7
        10331
        18
        Genus species
        MEGAHIT-CONCOCT-group-0_60
        3.3Kbp
        0.0Mbp
        4
        9619
        13
        Genus species
        MEGAHIT-CONCOCT-group-0_61
        1.8Kbp
        2.1Mbp
        1201
        2121564
        2158
        Genus species
        MEGAHIT-CONCOCT-group-0_62
        1.5Kbp
        1.4Mbp
        902
        1375240
        1437
        Genus species
        MEGAHIT-CONCOCT-group-0_63
        3.6Kbp
        0.0Mbp
        5
        16029
        10
        Genus species
        MEGAHIT-CONCOCT-group-0_64
        2.5Kbp
        0.0Mbp
        17
        43845
        12
        Genus species
        MEGAHIT-CONCOCT-group-0_65
        1.1Kbp
        0.0Mbp
        1
        1126
        Genus species
        MEGAHIT-CONCOCT-group-0_66
        3.0Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_67
        39.2Kbp
        5.5Mbp
        259
        5467423
        5283
        Genus species
        MEGAHIT-CONCOCT-group-0_68
        1.2Kbp
        0.0Mbp
        1
        1162
        Genus species
        MEGAHIT-MaxBin2-group-0.002
        9.9Kbp
        2.9Mbp
        MEGAHIT-MaxBin2-group-0.003
        21.3Kbp
        5.2Mbp
        406
        5167809
        4752
        Genus species
        MEGAHIT-MaxBin2-group-0.004
        2.6Kbp
        1.5Mbp
        643
        1525539
        1342
        Genus species
        MEGAHIT-MaxBin2-group-0.005
        2.8Kbp
        2.1Mbp
        857
        2055796
        1985
        Genus species
        MEGAHIT-MaxBin2-group-0.006
        2.5Kbp
        2.4Mbp
        1064
        2391141
        2498
        Genus species
        MEGAHIT-MaxBin2-group-0.007
        28.9Kbp
        0.6Mbp
        37
        590936
        663
        Genus species
        MEGAHIT-MaxBin2-group-0.008
        1.7Kbp
        2.2Mbp
        1266
        2151383
        2217
        Genus species
        MEGAHIT-MaxBin2-group-0.009
        42.7Kbp
        5.2Mbp
        308
        5169997
        4946
        Genus species
        MEGAHIT-MaxBin2-group-0.010
        2.2Kbp
        6.2Mbp
        3015
        6213497
        6504
        Genus species
        MEGAHIT-MaxBin2-group-0.011
        136.4Kbp
        4.9Mbp
        110
        4942137
        4661
        Genus species
        MEGAHIT-MaxBin2-group-0.012
        8.4Kbp
        2.2Mbp
        447
        2192949
        2496
        Genus species
        MEGAHIT-MaxBin2-group-0.014
        1.5Kbp
        3.2Mbp
        2041
        3190771
        3338
        Genus species
        MEGAHIT-MaxBin2-group-0.015
        27.1Kbp
        3.1Mbp
        266
        3080845
        3146
        Genus species
        MEGAHIT-MaxBin2-group-0.016
        3.2Kbp
        4.0Mbp
        1532
        3963653
        4260
        Genus species
        MEGAHIT-MaxBin2-group-0.017
        3.1Kbp
        7.3Mbp
        2879
        7343053
        7152
        Genus species
        MEGAHIT-MaxBin2-group-0.018
        3.4Kbp
        4.8Mbp
        1788
        4788021
        4717
        Genus species
        MEGAHIT-MetaBAT2-group-0.1
        239.3Kbp
        0.2Mbp
        1
        239287
        302
        Genus species
        MEGAHIT-MetaBAT2-group-0.2
        14.6Kbp
        0.5Mbp
        MEGAHIT-MetaBAT2-group-0.3
        2.4Kbp
        1.0Mbp
        410
        965548
        1034
        Genus species
        MEGAHIT-MetaBAT2-group-0.4
        32.7Kbp
        6.1Mbp
        416
        6127836
        5948
        Genus species
        MEGAHIT-MetaBAT2-group-0.6
        3.9Kbp
        2.6Mbp
        726
        2575429
        2540
        Genus species
        MEGAHIT-MetaBAT2-group-0.7
        8.8Kbp
        1.1Mbp
        190
        1117767
        1296
        Genus species
        MEGAHIT-MetaBAT2-group-0.8
        4.3Kbp
        7.2Mbp
        MEGAHIT-MetaBAT2-group-0.9
        15.6Kbp
        0.4Mbp
        MEGAHIT-MetaBAT2-group-0.10
        3.7Kbp
        2.7Mbp
        830
        2748250
        2637
        Genus species
        MEGAHIT-MetaBAT2-group-0.11
        5.8Kbp
        0.6Mbp
        MEGAHIT-MetaBAT2-group-0.12
        21.4Kbp
        5.4Mbp
        374
        5353146
        5047
        Genus species
        MEGAHIT-MetaBAT2-group-0.13
        2.1Kbp
        0.9Mbp
        447
        947955
        1013
        Genus species
        MEGAHIT-MetaBAT2-group-0.14
        4.3Kbp
        4.3Mbp
        1117
        4286792
        4085
        Genus species
        MEGAHIT-MetaBAT2-group-0.16
        172.6Kbp
        2.3Mbp
        19
        2261055
        2160
        Genus species
        MEGAHIT-MetaBAT2-group-0.17
        20.1Kbp
        2.8Mbp
        237
        2831914
        2879
        Genus species
        MEGAHIT-MetaBAT2-group-0.18
        15.4Kbp
        0.8Mbp
        MEGAHIT-MetaBAT2-group-0.19
        57.5Kbp
        0.2Mbp
        12
        210757
        231
        Genus species
        MEGAHIT-MetaBAT2-group-0.20
        170.5Kbp
        0.2Mbp
        2
        205308
        255
        Genus species
        MEGAHIT-MetaBAT2-group-0.21
        136.4Kbp
        2.2Mbp
        24
        2163733
        2007
        Genus species
        MEGAHIT-MetaBAT2-group-0.22
        11.1Kbp
        0.5Mbp
        112
        517680
        517
        Genus species
        MEGAHIT-MetaBAT2-group-0.23
        2.7Kbp
        1.9Mbp
        699
        1853137
        1951
        Genus species
        MEGAHIT-group-0
        8.6Kbp
        124.4Mbp
        MEGAHIT-group-0-T1
        98.6%
        MEGAHIT-group-0-T2
        98.2%
        MEGAHIT-group-0-T3
        99.1%
        MEGAHIT-group-0-T4
        97.7%
        MEGAHIT-group-0-T6
        98.5%
        MEGAHIT-group-0-T8
        97.0%
        T1_run0
        0.3%
        79.9%
        963.8Mb
        8.8M
        47.0%
        89.8%
        25.2%
        12.7%
        T1_run0_raw_1
        11.3%
        47.0%
        151bp
        151bp
        4.9M
        18%
        T1_run0_raw_2
        11.3%
        47.0%
        151bp
        151bp
        4.9M
        18%
        T1_run0_trimmed_1
        11.1%
        47.0%
        141bp
        151bp
        3.8M
        9%
        T1_run0_trimmed_2
        11.3%
        46.0%
        141bp
        151bp
        3.8M
        9%
        T2_run0
        0.3%
        80.7%
        505.0Mb
        4.6M
        46.4%
        89.8%
        29.7%
        24.4%
        T2_run0_raw_1
        8.6%
        47.0%
        151bp
        151bp
        2.6M
        18%
        T2_run0_raw_2
        8.0%
        46.0%
        151bp
        151bp
        2.6M
        18%
        T2_run0_trimmed_1
        7.6%
        46.0%
        143bp
        151bp
        1.8M
        9%
        T2_run0_trimmed_2
        7.2%
        46.0%
        143bp
        151bp
        1.8M
        9%
        T3_run0
        0.5%
        79.2%
        2373.3Mb
        21.3M
        47.4%
        88.6%
        20.9%
        1.7%
        T3_run0_raw_1
        18.7%
        48.0%
        151bp
        151bp
        12.0M
        18%
        T3_run0_raw_2
        20.3%
        47.0%
        151bp
        151bp
        12.0M
        18%
        T3_run0_trimmed_1
        18.4%
        47.0%
        141bp
        151bp
        10.5M
        9%
        T3_run0_trimmed_2
        21.0%
        47.0%
        141bp
        151bp
        10.5M
        9%
        T4_run0
        0.3%
        79.8%
        890.6Mb
        8.0M
        45.6%
        87.7%
        24.4%
        13.2%
        T4_run0_raw_1
        9.5%
        46.0%
        151bp
        151bp
        4.6M
        18%
        T4_run0_raw_2
        8.8%
        45.0%
        151bp
        151bp
        4.6M
        18%
        T4_run0_trimmed_1
        9.0%
        45.0%
        142bp
        151bp
        3.5M
        9%
        T4_run0_trimmed_2
        8.8%
        44.0%
        142bp
        151bp
        3.5M
        9%
        T6_run0
        0.2%
        79.2%
        555.4Mb
        5.1M
        47.0%
        87.5%
        27.7%
        17.3%
        T6_run0_raw_1
        8.7%
        48.0%
        151bp
        151bp
        2.9M
        18%
        T6_run0_raw_2
        7.7%
        47.0%
        151bp
        151bp
        2.9M
        9%
        T6_run0_trimmed_1
        8.4%
        47.0%
        142bp
        151bp
        2.1M
        9%
        T6_run0_trimmed_2
        7.7%
        46.0%
        142bp
        151bp
        2.1M
        9%
        T8_run0
        0.9%
        80.6%
        2651.6Mb
        23.1M
        40.5%
        90.5%
        18.8%
        1.6%
        T8_run0_raw_1
        31.6%
        41.0%
        151bp
        151bp
        12.7M
        27%
        T8_run0_raw_2
        31.4%
        40.0%
        151bp
        151bp
        12.7M
        27%
        T8_run0_trimmed_1
        31.9%
        40.0%
        143bp
        151bp
        11.4M
        18%
        T8_run0_trimmed_2
        33.4%
        40.0%
        143bp
        151bp
        11.4M
        18%

        FastQC: raw reads

        Quality control tool for high throughput sequencing data.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        All samples have sequences of a single length (151bp)

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        12 samples had less than 1% of reads made up of overrepresented sequences

        Top overrepresented sequences

        Top overrepresented sequences across all samples. The table shows 20 most overrepresented sequences across all samples, ranked by the number of samples they occur in.

        Showing 1/1 rows and 3/3 columns.
        Overrepresented sequenceReportsOccurrences% of all reads
        GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGG
        6
        62002
        0.0780%

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        fastp

        All-in-one FASTQ preprocessor (QC, adapters, trimming, filtering, splitting...).URL: https://github.com/OpenGene/fastpDOI: 10.1093/bioinformatics/bty560

        Fastp goes through fastq files in a folder and perform a series of quality control and filtering. Quality control and reporting are displayed both before and after filtering, allowing for a clear depiction of the consequences of the filtering process. Notably, the latter can be conducted on a variety of parameters including quality scores, length, as well as the presence of adapters, polyG, or polyX tailing.

        Filtered Reads

        Filtering statistics of sampled reads.

        Created with MultiQC

        Insert Sizes

        Insert size estimation of sampled reads.

        Created with MultiQC

        Sequence Quality

        Average sequencing quality over each base of all reads.

        Created with MultiQC

        GC Content

        Average GC content over each base of all reads.

        Created with MultiQC

        N content

        Average N content over each base of all reads.

        Created with MultiQC


        FastQC: after preprocessing

        After trimming and, if requested, contamination removal.URL: http://www.bioinformatics.babraham.ac.uk/projects/fastqc

        Sequence Counts

        Sequence counts for each sample. Duplicate read counts are an estimate only.

        This plot show the total number of reads, broken down into unique and duplicate if possible (only more recent versions of FastQC give duplicate info).

        You can read more about duplicate calculation in the FastQC documentation. A small part has been copied here for convenience:

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        Created with MultiQC

        Sequence Quality Histograms

        The mean quality value across each base position in the read.

        To enable multiple samples to be plotted on the same graph, only the mean quality scores are plotted (unlike the box plots seen in FastQC reports).

        Taken from the FastQC help:

        The y-axis on the graph shows the quality scores. The higher the score, the better the base call. The background of the graph divides the y axis into very good quality calls (green), calls of reasonable quality (orange), and calls of poor quality (red). The quality of calls on most platforms will degrade as the run progresses, so it is common to see base calls falling into the orange area towards the end of a read.

        Created with MultiQC

        Per Sequence Quality Scores

        The number of reads with average quality scores. Shows if a subset of reads has poor quality.

        From the FastQC help:

        The per sequence quality score report allows you to see if a subset of your sequences have universally low quality values. It is often the case that a subset of sequences will have universally poor quality, however these should represent only a small percentage of the total sequences.

        Created with MultiQC

        Per Base Sequence Content

        The proportion of each base position for which each of the four normal DNA bases has been called.

        To enable multiple samples to be shown in a single plot, the base composition data is shown as a heatmap. The colours represent the balance between the four bases: an even distribution should give an even muddy brown colour. Hover over the plot to see the percentage of the four bases under the cursor.

        To see the data as a line plot, as in the original FastQC graph, click on a sample track.

        From the FastQC help:

        Per Base Sequence Content plots out the proportion of each base position in a file for which each of the four normal DNA bases has been called.

        In a random library you would expect that there would be little to no difference between the different bases of a sequence run, so the lines in this plot should run parallel with each other. The relative amount of each base should reflect the overall amount of these bases in your genome, but in any case they should not be hugely imbalanced from each other.

        It's worth noting that some types of library will always produce biased sequence composition, normally at the start of the read. Libraries produced by priming using random hexamers (including nearly all RNA-Seq libraries) and those which were fragmented using transposases inherit an intrinsic bias in the positions at which reads start. This bias does not concern an absolute sequence, but instead provides enrichement of a number of different K-mers at the 5' end of the reads. Whilst this is a true technical bias, it isn't something which can be corrected by trimming and in most cases doesn't seem to adversely affect the downstream analysis.

        Click a sample row to see a line plot for that dataset.
        Rollover for sample name
        Position: -
        %T: -
        %C: -
        %A: -
        %G: -

        Per Sequence GC Content

        The average GC content of reads. Normal random library typically have a roughly normal distribution of GC content.

        From the FastQC help:

        This module measures the GC content across the whole length of each sequence in a file and compares it to a modelled normal distribution of GC content.

        In a normal random library you would expect to see a roughly normal distribution of GC content where the central peak corresponds to the overall GC content of the underlying genome. Since we don't know the GC content of the genome the modal GC content is calculated from the observed data and used to build a reference distribution.

        An unusually shaped distribution could indicate a contaminated library or some other kinds of biased subset. A normal distribution which is shifted indicates some systematic bias which is independent of base position. If there is a systematic bias which creates a shifted normal distribution then this won't be flagged as an error by the module since it doesn't know what your genome's GC content should be.

        Created with MultiQC

        Per Base N Content

        The percentage of base calls at each position for which an N was called.

        From the FastQC help:

        If a sequencer is unable to make a base call with sufficient confidence then it will normally substitute an N rather than a conventional base call. This graph shows the percentage of base calls at each position for which an N was called.

        It's not unusual to see a very low proportion of Ns appearing in a sequence, especially nearer the end of a sequence. However, if this proportion rises above a few percent it suggests that the analysis pipeline was unable to interpret the data well enough to make valid base calls.

        Created with MultiQC

        Sequence Length Distribution

        The distribution of fragment sizes (read lengths) found. See the FastQC help

        Created with MultiQC

        Sequence Duplication Levels

        The relative level of duplication found for every sequence.

        From the FastQC Help:

        In a diverse library most sequences will occur only once in the final set. A low level of duplication may indicate a very high level of coverage of the target sequence, but a high level of duplication is more likely to indicate some kind of enrichment bias (e.g. PCR over amplification). This graph shows the degree of duplication for every sequence in a library: the relative number of sequences with different degrees of duplication.

        Only sequences which first appear in the first 100,000 sequences in each file are analysed. This should be enough to get a good impression for the duplication levels in the whole file. Each sequence is tracked to the end of the file to give a representative count of the overall duplication level.

        The duplication detection requires an exact sequence match over the whole length of the sequence. Any reads over 75bp in length are truncated to 50bp for this analysis.

        In a properly diverse library most sequences should fall into the far left of the plot in both the red and blue lines. A general level of enrichment, indicating broad oversequencing in the library will tend to flatten the lines, lowering the low end and generally raising other categories. More specific enrichments of subsets, or the presence of low complexity contaminants will tend to produce spikes towards the right of the plot.

        Created with MultiQC

        Overrepresented sequences by sample

        The total amount of overrepresented sequences found in each library.

        FastQC calculates and lists overrepresented sequences in FastQ files. It would not be possible to show this for all samples in a MultiQC report, so instead this plot shows the number of sequences categorized as overrepresented.

        Sometimes, a single sequence may account for a large number of reads in a dataset. To show this, the bars are split into two: the first shows the overrepresented reads that come from the single most common sequence. The second shows the total count from all remaining overrepresented sequences.

        From the FastQC Help:

        A normal high-throughput library will contain a diverse set of sequences, with no individual sequence making up a tiny fraction of the whole. Finding that a single sequence is very overrepresented in the set either means that it is highly biologically significant, or indicates that the library is contaminated, or not as diverse as you expected.

        FastQC lists all the sequences which make up more than 0.1% of the total. To conserve memory only sequences which appear in the first 100,000 sequences are tracked to the end of the file. It is therefore possible that a sequence which is overrepresented but doesn't appear at the start of the file for some reason could be missed by this module.

        12 samples had less than 1% of reads made up of overrepresented sequences

        Adapter Content

        The cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position.

        Note that only samples with ≥ 0.1% adapter contamination are shown.

        There may be several lines per sample, as one is shown for each adapter detected in the file.

        From the FastQC Help:

        The plot shows a cumulative percentage count of the proportion of your library which has seen each of the adapter sequences at each position. Once a sequence has been seen in a read it is counted as being present right through to the end of the read so the percentages you see will only increase as the read length goes on.

        Created with MultiQC

        Status Checks

        Status for each FastQC section showing whether results seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        FastQC assigns a status for each section of the report. These give a quick evaluation of whether the results of the analysis seem entirely normal (green), slightly abnormal (orange) or very unusual (red).

        It is important to stress that although the analysis results appear to give a pass/fail result, these evaluations must be taken in the context of what you expect from your library. A 'normal' sample as far as FastQC is concerned is random and diverse. Some experiments may be expected to produce libraries which are biased in particular ways. You should treat the summary evaluations therefore as pointers to where you should concentrate your attention and understand why your library may not look random and diverse.

        Specific guidance on how to interpret the output of each module can be found in the relevant report section, or in the FastQC help.

        In this heatmap, we summarise all of these into a single heatmap for a quick overview. Note that not all FastQC sections have plots in MultiQC reports, but all status checks are shown in this heatmap.

        Created with MultiQC

        Bowtie2: host removal

        Mapping statistics of reads mapped against host genome and subsequently removed.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: assembly

        Assembly statistics of raw assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 1/1 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-group-0
        8.6Kbp
        1.7Kbp
        2.0K
        11.3K
        502.7Kbp
        124.4Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        Bowtie2: assembly

        Mapping statistics of reads mapped against assemblies.URL: http://bowtie-bio.sourceforge.net/bowtie2; https://ccb.jhu.edu/software/hisat2DOI: 10.1038/nmeth.1923; 10.1038/nmeth.3317; 10.1038/s41587-019-0201-4

        Paired-end alignments

        This plot shows the number of reads aligning to the reference in different ways.

        Please note that single mate alignment counts are halved to tally with pair counts properly.

        There are 6 possible types of alignment:

        • PE mapped uniquely: Pair has only one occurence in the reference genome.
        • PE mapped discordantly uniquely: Pair has only one occurence but not in proper pair.
        • PE one mate mapped uniquely: One read of a pair has one occurence.
        • PE multimapped: Pair has multiple occurence.
        • PE one mate multimapped: One read of a pair has multiple occurence.
        • PE neither mate aligned: Pair has no occurence.
        Created with MultiQC

        QUAST: bins

        Assembly statistics of binned assemblies.URL: http://quast.bioinf.spbau.ruDOI: 10.1093/bioinformatics/btt086

        Assembly Statistics

        Showing 104/104 rows and 6/6 columns.
        Sample NameN50 (Kbp)N75 (Kbp)L50 (K)L75 (K)Largest contig (Kbp)Length (Mbp)
        MEGAHIT-CONCOCT-group-0_0
        1.7Kbp
        1.7Kbp
        0.0K
        0.0K
        1.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_1
        4.8Kbp
        3.2Kbp
        0.2K
        0.4K
        17.9Kbp
        3.1Mbp
        MEGAHIT-CONCOCT-group-0_2
        1.8Kbp
        1.3Kbp
        0.4K
        0.7K
        6.1Kbp
        2.0Mbp
        MEGAHIT-CONCOCT-group-0_3
        14.8Kbp
        14.8Kbp
        0.0K
        0.0K
        14.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_4
        1.3Kbp
        1.1Kbp
        0.0K
        0.1K
        6.1Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_5
        1.5Kbp
        1.2Kbp
        1.5K
        2.8K
        5.8Kbp
        6.6Mbp
        MEGAHIT-CONCOCT-group-0_6
        2.0Kbp
        1.3Kbp
        0.0K
        0.0K
        4.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_7
        1.4Kbp
        1.0Kbp
        0.0K
        0.0K
        1.4Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_8
        1.0Kbp
        1.0Kbp
        0.0K
        0.0K
        1.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_9
        3.1Kbp
        3.1Kbp
        0.0K
        0.0K
        3.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_10
        9.2Kbp
        5.3Kbp
        0.3K
        0.6K
        52.2Kbp
        9.4Mbp
        MEGAHIT-CONCOCT-group-0_11
        170.5Kbp
        170.5Kbp
        0.0K
        0.0K
        170.5Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_12
        1.8Kbp
        1.2Kbp
        0.0K
        0.0K
        7.1Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_13
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_14
        1.3Kbp
        1.1Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_15
        1.4Kbp
        1.2Kbp
        0.1K
        0.2K
        3.5Kbp
        0.3Mbp
        MEGAHIT-CONCOCT-group-0_16
        1.7Kbp
        1.3Kbp
        0.9K
        1.5K
        6.4Kbp
        4.1Mbp
        MEGAHIT-CONCOCT-group-0_17
        4.5Kbp
        2.1Kbp
        0.0K
        0.0K
        11.7Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_18
        1.9Kbp
        1.4Kbp
        0.3K
        0.6K
        10.4Kbp
        1.8Mbp
        MEGAHIT-CONCOCT-group-0_19
        1.5Kbp
        1.4Kbp
        0.0K
        0.0K
        1.5Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_20
        1.1Kbp
        1.0Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_21
        2.7Kbp
        1.7Kbp
        0.1K
        0.3K
        29.7Kbp
        1.1Mbp
        MEGAHIT-CONCOCT-group-0_22
        239.3Kbp
        239.3Kbp
        0.0K
        0.0K
        239.3Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_24
        26.7Kbp
        9.9Kbp
        0.0K
        0.0K
        26.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_25
        2.0Kbp
        1.4Kbp
        0.0K
        0.0K
        5.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_26
        1.3Kbp
        1.1Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_27
        1.2Kbp
        1.1Kbp
        0.0K
        0.0K
        2.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_28
        4.2Kbp
        1.5Kbp
        0.0K
        0.0K
        5.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_29
        1.8Kbp
        1.4Kbp
        0.6K
        1.1K
        6.1Kbp
        3.2Mbp
        MEGAHIT-CONCOCT-group-0_30
        9.2Kbp
        2.4Kbp
        0.0K
        0.0K
        26.2Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_31
        1.0Kbp
        1.0Kbp
        0.0K
        0.0K
        1.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_32
        25.5Kbp
        12.5Kbp
        0.0K
        0.1K
        100.8Kbp
        2.9Mbp
        MEGAHIT-CONCOCT-group-0_33
        1.7Kbp
        1.3Kbp
        0.0K
        0.0K
        8.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_34
        2.3Kbp
        1.8Kbp
        0.0K
        0.0K
        4.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_35
        1.3Kbp
        1.2Kbp
        0.0K
        0.0K
        1.8Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_36
        2.9Kbp
        1.1Kbp
        0.0K
        0.0K
        2.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_37
        1.4Kbp
        1.2Kbp
        0.1K
        0.1K
        7.6Kbp
        0.2Mbp
        MEGAHIT-CONCOCT-group-0_38
        1.6Kbp
        1.6Kbp
        0.0K
        0.0K
        1.6Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_39
        7.7Kbp
        5.5Kbp
        0.2K
        0.4K
        50.6Kbp
        4.7Mbp
        MEGAHIT-CONCOCT-group-0_40
        1.4Kbp
        1.1Kbp
        0.0K
        0.0K
        1.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_41
        1.3Kbp
        1.1Kbp
        0.0K
        0.0K
        3.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_42
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_43
        30.4Kbp
        30.4Kbp
        0.0K
        0.0K
        30.4Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_44
        14.8Kbp
        5.8Kbp
        0.1K
        0.3K
        194.9Kbp
        7.3Mbp
        MEGAHIT-CONCOCT-group-0_45
        1.2Kbp
        1.1Kbp
        0.0K
        0.0K
        2.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_46
        14.6Kbp
        6.0Kbp
        0.1K
        0.1K
        59.2Kbp
        2.5Mbp
        MEGAHIT-CONCOCT-group-0_47
        1.4Kbp
        1.3Kbp
        0.0K
        0.0K
        1.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_48
        5.3Kbp
        3.6Kbp
        0.2K
        0.5K
        25.2Kbp
        3.9Mbp
        MEGAHIT-CONCOCT-group-0_49
        2.2Kbp
        2.2Kbp
        0.0K
        0.0K
        2.2Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_50
        1.3Kbp
        1.3Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_51
        1.8Kbp
        1.3Kbp
        0.0K
        0.0K
        3.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_52
        1.3Kbp
        1.1Kbp
        0.0K
        0.0K
        1.3Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_53
        1.9Kbp
        1.4Kbp
        0.0K
        0.0K
        3.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_54
        2.6Kbp
        1.7Kbp
        0.2K
        0.4K
        27.1Kbp
        1.9Mbp
        MEGAHIT-CONCOCT-group-0_55
        136.4Kbp
        78.4Kbp
        0.0K
        0.0K
        437.3Kbp
        4.9Mbp
        MEGAHIT-CONCOCT-group-0_56
        2.7Kbp
        1.3Kbp
        0.0K
        0.0K
        7.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_57
        1.4Kbp
        1.1Kbp
        0.0K
        0.0K
        3.9Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_58
        1.2Kbp
        1.1Kbp
        0.0K
        0.0K
        2.9Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_60
        3.3Kbp
        1.5Kbp
        0.0K
        0.0K
        3.7Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_61
        1.8Kbp
        1.4Kbp
        0.4K
        0.7K
        19.7Kbp
        2.1Mbp
        MEGAHIT-CONCOCT-group-0_62
        1.5Kbp
        1.2Kbp
        0.3K
        0.6K
        5.8Kbp
        1.4Mbp
        MEGAHIT-CONCOCT-group-0_63
        3.6Kbp
        2.6Kbp
        0.0K
        0.0K
        7.0Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_64
        2.5Kbp
        2.0Kbp
        0.0K
        0.0K
        10.6Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_65
        1.1Kbp
        1.1Kbp
        0.0K
        0.0K
        1.1Kbp
        0.0Mbp
        MEGAHIT-CONCOCT-group-0_66
        3.0Kbp
        2.3Kbp
        0.0K
        0.0K
        13.5Kbp
        0.1Mbp
        MEGAHIT-CONCOCT-group-0_67
        39.2Kbp
        18.1Kbp
        0.0K
        0.1K
        129.7Kbp
        5.5Mbp
        MEGAHIT-CONCOCT-group-0_68
        1.2Kbp
        1.2Kbp
        0.0K
        0.0K
        1.2Kbp
        0.0Mbp
        MEGAHIT-MaxBin2-group-0.002
        9.9Kbp
        3.0Kbp
        0.1K
        0.2K
        59.2Kbp
        2.9Mbp
        MEGAHIT-MaxBin2-group-0.003
        21.3Kbp
        12.3Kbp
        0.1K
        0.1K
        194.9Kbp
        5.2Mbp
        MEGAHIT-MaxBin2-group-0.004
        2.6Kbp
        1.6Kbp
        0.2K
        0.3K
        29.8Kbp
        1.5Mbp
        MEGAHIT-MaxBin2-group-0.005
        2.8Kbp
        1.8Kbp
        0.2K
        0.5K
        21.0Kbp
        2.1Mbp
        MEGAHIT-MaxBin2-group-0.006
        2.5Kbp
        1.6Kbp
        0.3K
        0.6K
        42.0Kbp
        2.4Mbp
        MEGAHIT-MaxBin2-group-0.007
        28.9Kbp
        15.8Kbp
        0.0K
        0.0K
        239.3Kbp
        0.6Mbp
        MEGAHIT-MaxBin2-group-0.008
        1.7Kbp
        1.3Kbp
        0.4K
        0.8K
        6.5Kbp
        2.2Mbp
        MEGAHIT-MaxBin2-group-0.009
        42.7Kbp
        18.1Kbp
        0.0K
        0.1K
        129.7Kbp
        5.2Mbp
        MEGAHIT-MaxBin2-group-0.010
        2.2Kbp
        1.4Kbp
        0.8K
        1.7K
        16.6Kbp
        6.2Mbp
        MEGAHIT-MaxBin2-group-0.011
        136.4Kbp
        78.4Kbp
        0.0K
        0.0K
        437.3Kbp
        4.9Mbp
        MEGAHIT-MaxBin2-group-0.012
        8.4Kbp
        4.0Kbp
        0.1K
        0.2K
        57.5Kbp
        2.2Mbp
        MEGAHIT-MaxBin2-group-0.014
        1.5Kbp
        1.2Kbp
        0.7K
        1.3K
        26.7Kbp
        3.2Mbp
        MEGAHIT-MaxBin2-group-0.015
        27.1Kbp
        11.5Kbp
        0.0K
        0.1K
        170.5Kbp
        3.1Mbp
        MEGAHIT-MaxBin2-group-0.016
        3.2Kbp
        1.7Kbp
        0.3K
        0.8K
        28.3Kbp
        4.0Mbp
        MEGAHIT-MaxBin2-group-0.017
        3.1Kbp
        1.7Kbp
        0.6K
        1.5K
        25.2Kbp
        7.3Mbp
        MEGAHIT-MaxBin2-group-0.018
        3.4Kbp
        1.8Kbp
        0.4K
        0.9K
        23.8Kbp
        4.8Mbp
        MEGAHIT-MetaBAT2-group-0.1
        239.3Kbp
        239.3Kbp
        0.0K
        0.0K
        239.3Kbp
        0.2Mbp
        MEGAHIT-MetaBAT2-group-0.2
        14.6Kbp
        6.5Kbp
        0.0K
        0.0K
        59.2Kbp
        0.5Mbp
        MEGAHIT-MetaBAT2-group-0.3
        2.4Kbp
        1.9Kbp
        0.2K
        0.3K
        8.9Kbp
        1.0Mbp
        MEGAHIT-MetaBAT2-group-0.4
        32.7Kbp
        15.0Kbp
        0.1K
        0.1K
        129.7Kbp
        6.1Mbp
        MEGAHIT-MetaBAT2-group-0.6
        3.9Kbp
        2.5Kbp
        0.2K
        0.4K
        17.8Kbp
        2.6Mbp
        MEGAHIT-MetaBAT2-group-0.7
        8.8Kbp
        4.8Kbp
        0.0K
        0.1K
        31.9Kbp
        1.1Mbp
        MEGAHIT-MetaBAT2-group-0.8
        4.3Kbp
        2.5Kbp
        0.5K
        1.1K
        27.4Kbp
        7.2Mbp
        MEGAHIT-MetaBAT2-group-0.9
        15.6Kbp
        4.9Kbp
        0.0K
        0.0K
        48.2Kbp
        0.4Mbp
        MEGAHIT-MetaBAT2-group-0.10
        3.7Kbp
        2.5Kbp
        0.2K
        0.5K
        47.3Kbp
        2.7Mbp
        MEGAHIT-MetaBAT2-group-0.11
        5.8Kbp
        3.2Kbp
        0.0K
        0.1K
        25.7Kbp
        0.6Mbp
        MEGAHIT-MetaBAT2-group-0.12
        21.4Kbp
        13.0Kbp
        0.1K
        0.1K
        194.9Kbp
        5.4Mbp
        MEGAHIT-MetaBAT2-group-0.13
        2.1Kbp
        1.8Kbp
        0.2K
        0.3K
        5.9Kbp
        0.9Mbp
        MEGAHIT-MetaBAT2-group-0.14
        4.3Kbp
        2.8Kbp
        0.3K
        0.6K
        25.2Kbp
        4.3Mbp
        MEGAHIT-MetaBAT2-group-0.16
        172.6Kbp
        112.7Kbp
        0.0K
        0.0K
        437.3Kbp
        2.3Mbp
        MEGAHIT-MetaBAT2-group-0.17
        20.1Kbp
        10.7Kbp
        0.0K
        0.1K
        100.8Kbp
        2.8Mbp
        MEGAHIT-MetaBAT2-group-0.18
        15.4Kbp
        7.3Kbp
        0.0K
        0.0K
        56.4Kbp
        0.8Mbp
        MEGAHIT-MetaBAT2-group-0.19
        57.5Kbp
        16.5Kbp
        0.0K
        0.0K
        58.5Kbp
        0.2Mbp
        MEGAHIT-MetaBAT2-group-0.20
        170.5Kbp
        170.5Kbp
        0.0K
        0.0K
        170.5Kbp
        0.2Mbp
        MEGAHIT-MetaBAT2-group-0.21
        136.4Kbp
        78.5Kbp
        0.0K
        0.0K
        269.5Kbp
        2.2Mbp
        MEGAHIT-MetaBAT2-group-0.22
        11.1Kbp
        2.3Kbp
        0.0K
        0.0K
        78.4Kbp
        0.5Mbp
        MEGAHIT-MetaBAT2-group-0.23
        2.7Kbp
        2.0Kbp
        0.2K
        0.4K
        13.7Kbp
        1.9Mbp

        Number of Contigs

        This plot shows the number of contigs found for each assembly, broken down by length.

        Created with MultiQC

        CheckM

        Estimates genome completeness and contamination based on the presence or absence of marker genes.URL: https://github.com/Ecogenomics/CheckMDOI: 10.1101/gr.186072.114

        Bin quality

        The quality of microbial genomes recovered from isolates, single cells, and metagenomes.

        An automated method for assessing the quality of a genome using a broader set of marker genes specific to the position of a genome within a reference genome tree and information about the collocation of these genes.

        Showing 88/88 rows and 6/6 columns.
        Bin IdMarker lineageGenomesMarkersMarker setsCompletenessContamination
        MEGAHIT-CONCOCT-group-0_0
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_1
        f__Paenibacillaceae (UID973)
        32
        468
        170
        43.62%
        10.45%
        MEGAHIT-CONCOCT-group-0_2
        k__Bacteria (UID203)
        5449
        104
        58
        37.93%
        1.72%
        MEGAHIT-CONCOCT-group-0_3
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_4
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_5
        f__Paenibacillaceae (UID971)
        46
        481
        186
        74.46%
        53.57%
        MEGAHIT-CONCOCT-group-0_6
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_7
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_8
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_11
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_13
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_14
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_15
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_16
        f__Paenibacillaceae (UID973)
        32
        468
        170
        53.77%
        18.65%
        MEGAHIT-CONCOCT-group-0_17
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_18
        k__Bacteria (UID203)
        5449
        101
        56
        41.56%
        9.82%
        MEGAHIT-CONCOCT-group-0_19
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_20
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_22
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_24
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_25
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_26
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_27
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_29
        k__Bacteria (UID203)
        5449
        104
        58
        87.93%
        55.52%
        MEGAHIT-CONCOCT-group-0_31
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_32
        k__Bacteria (UID203)
        5449
        104
        58
        96.55%
        9.56%
        MEGAHIT-CONCOCT-group-0_33
        k__Bacteria (UID203)
        5449
        101
        56
        2.27%
        0.00%
        MEGAHIT-CONCOCT-group-0_35
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_36
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_37
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_38
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_39
        k__Bacteria (UID203)
        5449
        103
        57
        47.37%
        2.63%
        MEGAHIT-CONCOCT-group-0_40
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_41
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_42
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_43
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_44
        k__Bacteria (UID203)
        5449
        104
        58
        89.18%
        134.45%
        MEGAHIT-CONCOCT-group-0_45
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_47
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_48
        k__Bacteria (UID203)
        5449
        104
        58
        49.11%
        0.00%
        MEGAHIT-CONCOCT-group-0_49
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_50
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_51
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_52
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_53
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_54
        f__Bacillaceae (UID829)
        128
        559
        183
        5.40%
        0.13%
        MEGAHIT-CONCOCT-group-0_55
        f__Enterobacteriaceae (UID5124)
        134
        1173
        336
        99.07%
        0.65%
        MEGAHIT-CONCOCT-group-0_56
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_58
        o__Bacillales (UID828)
        139
        508
        174
        0.36%
        0.08%
        MEGAHIT-CONCOCT-group-0_60
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_61
        o__Bacillales (UID828)
        139
        508
        174
        60.50%
        1.27%
        MEGAHIT-CONCOCT-group-0_62
        k__Bacteria (UID203)
        5449
        103
        57
        32.43%
        17.37%
        MEGAHIT-CONCOCT-group-0_63
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_64
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_65
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-CONCOCT-group-0_67
        f__Bacillaceae (UID829)
        128
        559
        183
        91.43%
        2.74%
        MEGAHIT-CONCOCT-group-0_68
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MaxBin2-group-0.003
        c__Bacilli (UID285)
        586
        325
        181
        94.11%
        73.40%
        MEGAHIT-MaxBin2-group-0.004
        k__Bacteria (UID203)
        5449
        104
        58
        88.06%
        61.44%
        MEGAHIT-MaxBin2-group-0.005
        k__Bacteria (UID203)
        5449
        104
        58
        33.97%
        6.90%
        MEGAHIT-MaxBin2-group-0.006
        k__Bacteria (UID203)
        5449
        104
        58
        19.83%
        2.59%
        MEGAHIT-MaxBin2-group-0.007
        root (UID1)
        5656
        56
        24
        4.17%
        0.00%
        MEGAHIT-MaxBin2-group-0.008
        k__Bacteria (UID203)
        5449
        104
        58
        62.30%
        16.07%
        MEGAHIT-MaxBin2-group-0.009
        f__Bacillaceae (UID829)
        128
        559
        183
        87.50%
        2.59%
        MEGAHIT-MaxBin2-group-0.010
        f__Paenibacillaceae (UID973)
        32
        468
        170
        78.73%
        45.75%
        MEGAHIT-MaxBin2-group-0.011
        f__Enterobacteriaceae (UID5124)
        134
        1173
        336
        99.07%
        0.69%
        MEGAHIT-MaxBin2-group-0.012
        k__Bacteria (UID203)
        5449
        103
        57
        2.63%
        0.00%
        MEGAHIT-MaxBin2-group-0.014
        g__Bacillus (UID865)
        36
        1200
        269
        64.83%
        22.08%
        MEGAHIT-MaxBin2-group-0.015
        k__Bacteria (UID203)
        5449
        104
        58
        98.28%
        34.78%
        MEGAHIT-MaxBin2-group-0.016
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MaxBin2-group-0.017
        k__Bacteria (UID203)
        5449
        104
        58
        90.36%
        44.28%
        MEGAHIT-MaxBin2-group-0.018
        c__Bacilli (UID259)
        750
        275
        151
        70.49%
        16.78%
        MEGAHIT-MetaBAT2-group-0.1
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.3
        k__Bacteria (UID203)
        5449
        103
        57
        19.30%
        0.00%
        MEGAHIT-MetaBAT2-group-0.4
        f__Bacillaceae (UID829)
        128
        559
        183
        92.78%
        3.59%
        MEGAHIT-MetaBAT2-group-0.6
        k__Bacteria (UID203)
        5449
        104
        58
        45.45%
        0.00%
        MEGAHIT-MetaBAT2-group-0.7
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.10
        k__Bacteria (UID203)
        5449
        104
        58
        60.83%
        20.45%
        MEGAHIT-MetaBAT2-group-0.12
        k__Bacteria (UID203)
        5449
        104
        58
        89.58%
        89.47%
        MEGAHIT-MetaBAT2-group-0.13
        k__Bacteria (UID203)
        5449
        104
        58
        29.18%
        1.72%
        MEGAHIT-MetaBAT2-group-0.14
        k__Bacteria (UID203)
        5449
        104
        58
        56.69%
        1.72%
        MEGAHIT-MetaBAT2-group-0.16
        root (UID1)
        5656
        56
        24
        12.50%
        0.00%
        MEGAHIT-MetaBAT2-group-0.17
        k__Bacteria (UID203)
        5449
        104
        58
        96.55%
        19.67%
        MEGAHIT-MetaBAT2-group-0.19
        k__Bacteria (UID203)
        5449
        103
        57
        0.88%
        0.00%
        MEGAHIT-MetaBAT2-group-0.20
        root (UID1)
        5656
        56
        24
        0.00%
        0.00%
        MEGAHIT-MetaBAT2-group-0.21
        f__Enterobacteriaceae (UID5124)
        134
        1173
        336
        53.21%
        0.06%
        MEGAHIT-MetaBAT2-group-0.22
        f__Enterobacteriaceae (UID5124)
        134
        1172
        336
        6.41%
        0.09%
        MEGAHIT-MetaBAT2-group-0.23
        f__Paenibacillaceae (UID973)
        32
        468
        170
        39.13%
        1.37%

        Prokka

        Rapid annotation of prokaryotic genomes.URL: http://www.vicbioinformatics.com/software.prokka.shtmlDOI: 10.1093/bioinformatics/btu153

        This barplot shows the distribution of different types of features found in each contig.

        Prokka can detect different features:

        • CDS
        • rRNA
        • tmRNA
        • tRNA
        • miscRNA
        • signal peptides
        • CRISPR arrays

        This barplot shows you the distribution of these different types of features found in each contig.

        Created with MultiQC

        GTDB-Tk

        Assigns objective taxonomic classifications to bacterial and archaeal genomes.URL: https://ecogenomics.github.io/GTDBTk/index.htmlDOI: 10.1093/bioinformatics/btac672

        MAG taxonomy

        The taxonomy of a MAG as found by GTDB.

        GTDB-Tk is a software toolkit for assigning objective taxonomic classifications to bacterial and archaeal genomes based on the Genome Database Taxonomy GTDB. It is designed to work with recent advances that allow hundreds or thousands of metagenome-assembled genomes (MAGs) to be obtained directly from environmental samples. It can also be applied to isolate and single-cell genomes.

        Showing 16/16 rows and 6/8 columns.
        User genomeClassificationFull classificationClassification methodANI to closest genomeAF to closest genomeREDWarningsNotes
        MEGAHIT-CONCOCT-group-0_2.fa
        s__Paenibacillus cookii
        d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus cookii
        ani_screen
        98.8
        0.9
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_18.fa
        s__
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_G; g__Bacillus_A; s__
        taxonomic classification defined by topology and ANI
        1.0
        MEGAHIT-CONCOCT-group-0_32.fa
        s__Kurthia gibsonii
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_A; f__Planococcaceae; g__Kurthia; s__Kurthia gibsonii
        ani_screen
        98.7
        0.9
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_39.fa
        s__
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_A; f__Planococcaceae; g__Lysinibacillus; s__
        taxonomic classification defined by topology and ANI
        1.0
        MEGAHIT-CONCOCT-group-0_48.fa
        s__Paenibacillus polymyxa
        d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus polymyxa
        ani_screen
        98.0
        0.8
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_55.fa
        s__Enterobacter cloacae_M
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M
        ani_screen
        98.2
        0.9
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_61.fa
        s__Priestia flexa
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales; f__Bacillaceae_H; g__Priestia; s__Priestia flexa
        ani_screen
        99.5
        0.8
        classification based on ANI only
        MEGAHIT-CONCOCT-group-0_67.fa
        s__Niallia circulans_A
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A
        ani_screen
        99.5
        0.9
        classification based on ANI only
        MEGAHIT-MaxBin2-group-0.005.fa
        s__
        d__Bacteria; p__Bacillota; c__Bacilli; o__Staphylococcales; f__Staphylococcaceae; g__Staphylococcus; s__
        taxonomic classification defined by topology and ANI
        1.0
        Genome has more than 14.2% of markers with multiple hits
        MEGAHIT-MaxBin2-group-0.009.fa
        s__Niallia circulans_A
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A
        ani_screen
        99.4
        0.9
        classification based on ANI only
        MEGAHIT-MaxBin2-group-0.011.fa
        s__Enterobacter cloacae_M
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M
        ani_screen
        98.2
        0.9
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.4.fa
        s__Niallia circulans_A
        d__Bacteria; p__Bacillota; c__Bacilli; o__Bacillales_B; f__DSM-18226; g__Niallia; s__Niallia circulans_A
        ani_screen
        99.4
        0.9
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.6.fa
        s__
        d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__
        taxonomic classification defined by topology and ANI
        1.0
        MEGAHIT-MetaBAT2-group-0.14.fa
        s__Paenibacillus polymyxa
        d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus polymyxa
        ani_screen
        98.1
        0.7
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.21.fa
        s__Enterobacter cloacae_M
        d__Bacteria; p__Pseudomonadota; c__Gammaproteobacteria; o__Enterobacterales; f__Enterobacteriaceae; g__Enterobacter; s__Enterobacter cloacae_M
        ani_screen
        98.5
        0.9
        classification based on ANI only
        MEGAHIT-MetaBAT2-group-0.23.fa
        s__Paenibacillus cookii
        d__Bacteria; p__Bacillota; c__Bacilli; o__Paenibacillales; f__Paenibacillaceae; g__Paenibacillus; s__Paenibacillus cookii
        ani_screen
        98.7
        0.9
        classification based on ANI only

        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        ADJUST_MAXBIN2_EXTcoreutils9.5
        BIN_SUMMARYpandas1.4.3
        python3.10.6
        BOWTIE2_ASSEMBLY_ALIGNbowtie22.4.2
        pigz2.3.4
        samtools1.11
        BOWTIE2_ASSEMBLY_BUILDbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_ALIGNbowtie22.4.2
        BOWTIE2_HOST_REMOVAL_BUILDbowtie22.4.2
        CHECKM_LINEAGEWFcheckm1.2.3
        CHECKM_QAcheckm1.2.3
        CONCAT_BINQC_TSVcsvtk0.31.0
        CONCOCT_CONCOCTconcoct1.1.0
        CONCOCT_CONCOCTCOVERAGETABLEconcoct1.1.0
        CONCOCT_CUTUPFASTAconcoct1.1.0
        CONCOCT_EXTRACTFASTABINSconcoct1.1.0
        CONCOCT_MERGECUTUPCLUSTERINGconcoct1.1.0
        CONVERT_DEPTHSbioawk20110810
        DASTOOL_DASTOOLdastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_CONCOCTdastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_MAXBIN2dastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_METABAT2dastool1.1.7
        DASTOOL_FASTATOCONTIG2BIN_TIARAdastool1.1.7
        FASTQC_RAWfastqc0.12.1
        FASTQC_TRIMMEDfastqc0.12.1
        GENOMAD_ENDTOENDgenomad1.11.0
        GTDBTK_CLASSIFYWFgtdb_dbr226
        gtdbtk2.5.2
        GTDBTK_SUMMARYpandas1.4.3
        python3.10.6
        GUNZIP_BINSgunzip1.13
        GUNZIP_SHORTREAD_ASSEMBLIESgunzip1.13
        GUNZIP_UNBINSgunzip1.13
        MAG_DEPTHSpandas1.1.5
        python3.6.7
        MAG_DEPTHS_PLOTpandas1.3.0
        python3.9.6
        seaborn0.11.0
        MAG_DEPTHS_SUMMARYpandas1.4.3
        python3.10.6
        MAXBIN2maxbin22.2.7
        MEGAHITmegahit1.2.9
        METABAT2_JGISUMMARIZEBAMCONTIGDEPTHS_SHORTREADmetabat22.15
        METABAT2_METABAT2metabat22.17
        PRODIGALpigz2.6
        prodigal2.6.3
        Prokkaprokka1.14.6
        QUASTmetaquast5.0.2
        python3.7.6
        QUAST_BINSmetaquast5.0.2
        python3.7.6
        QUAST_BINS_SUMMARYcp9.5
        RENAME_POSTDASTOOLcoreutils9.5
        RENAME_PREDASTOOLcoreutils9.5
        SEQKIT_STATSseqkit2.9.0
        SPLIT_FASTAbiopython1.7.4
        pandas1.1.5
        python3.6.7
        TIARA_SUMMARYcsvtk0.31.0
        TIARA_TIARAtiara1.0.3
        WorkflowNextflow25.04.8
        nf-core/magv5.0.0-g3d41222
        fastpfastp0.24.0
        r-baser-base4.1.3
        r-tidyverser-tidyverse1.3.1

        nf-core/mag Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/nf-core/mag

        Methods

        Data was processed using nf-core/mag v5.0.0 ((doi: 10.1093/nargab/lqac007); Krakau et al., 2022) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v25.04.8 (Di Tommaso et al., 2017) with the following command:

        nextflow run 'https://github.com/nf-core/mag' -name anna_miso_batch1_3 -params-file 'https://api.cloud.seqera.io/ephemeral/YTqQQMzoHmADjbz_X_QShw.json' -with-tower -r 3d41222776d2ed7a78dd3fd4dd643690c49f6bd2 -resume a4414311-4649-4eac-9943-0ba7e73b85f0

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Krakau, S., Straub, D., Gourlé, H., Gabernet, G., & Nahnsen, S. (2022). nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning. NAR Genomics and Bioinformatics, 4(1). https://doi.org/10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.

        nf-core/mag Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/nf-core/mag

        Input/output options

        email
        annalifousi@gmail.com
        input
        az://seqera/results/anna-mags/miso_batch1.csv
        multiqc_title
        MultiQC_miso_batch1_verysensitive
        outdir
        az://seqera/results/anna-mags/results_miso_new/

        Quality control for short reads options

        host_fasta
        az://seqera/raw/miso/miso_genome/miso_genome.fna
        host_removal_save_ids
        true
        host_removal_verysensitive
        true
        keep_phix
        true
        save_clipped_reads
        true
        save_hostremoved_reads
        true

        Quality control for long reads options

        longreads_min_quality
        1

        Taxonomic profiling options

        cat_db
        az://seqera/databases/cat/20241212_CAT_nr_website/
        gtdb_db
        az://seqera/databases/gtdb/GTDB/gtdbtk_package/full_package/release226/
        gtdbtk_max_contamination
        10.0
        gtdbtk_min_completeness
        30
        gtdbtk_use_full_tree
        true

        Assembly options

        coassemble_group
        true
        skip_flye
        true
        skip_metamdbg
        true
        skip_spades
        true
        skip_spadeshybrid
        true

        Gene prediction and annotation options

        skip_metaeuk
        true

        Virus identification options

        genomad_db
        az://seqera/databases/geNomadDB/14886553/genomad_db/
        run_virus_identification
        true

        Binning options

        bin_domain_classification
        true
        bin_max_size
        10000000
        exclude_unbins_from_postbinning
        true
        min_length_unbinned_contigs
        20000
        save_assembly_mapped_reads
        true

        Bin quality check options

        binqc_tool
        checkm
        busco_db_lineage
        N/A
        checkm_db
        az://seqera/databases/checkm_data/
        gunc_save_db
        true
        refine_bins_dastool
        true

        Core Nextflow options

        configFiles
        /.nextflow/assets/nf-core/mag/nextflow.config, /mnt/batch/tasks/workitems/nf-workflow-5VD8wN49lBdb1P/job-1/nf-workflow-5VD8wN49lBdb1P/wd/nextflow.config
        launchDir
        /mnt/batch/tasks/workitems/nf-workflow-5VD8wN49lBdb1P/job-1/nf-workflow-5VD8wN49lBdb1P/wd
        profile
        standard
        projectDir
        /.nextflow/assets/nf-core/mag
        revision
        5.0.0
        runName
        anna_miso_batch1_3
        userName
        root
        workDir
        /seqera/results/anna-mags/work